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Data Analysis September 2025

Bike Sharing Demand Prediction

Bike Sharing Demand hero image
This project explores the Capital Bikeshare dataset (Washington D.C., 2011–2012) to predict bike rental demand using supervised machine learning models. I applied a complete ML workflow: data cleaning, exploratory analysis, feature engineering, baseline comparison, model training, and hyperparameter tuning with cross-validation. Key Skills: Data Cleaning (Pandas), Machine Learning (Scikit-Learn), Hyperparameter Tuning (GridSearchCV), Model Deployment (Streamlit, GitHub).

Team

Sheila Géa

Duration

1 week
September 2025

Project Type

Data Analysis · Data Cleaning · Dashboard Design · API Integration · Machine learning

Machine Learning: Bike Sharing App

*You'll need to "wake up" the app.

View the app on Streamlit
Case study slide 1

About This Project

This project explores the Bike Sharing Dataset from the Capital Bikeshare system in Washington D.C. (2011–2012). The goal was to predict bike rental demand using supervised machine learning models.

Source:

  • Fanaee-T, Hadi, and Gama, João, “Event labeling combining ensemble detectors and background knowledge”,Progress in Artificial Intelligence (2013).

    • UCI ML Repository:

  • https://archive.ics.uci.edu/dataset/275/bike+sharing+dataset